Fachgebiet Wissensverarbeitung (KDE), EECS, Universität Kassel

Das Fachgebiet Wissensverarbeitung des Fachbereichs Elektrotechnik/Informatik forscht an der Entwicklung von Methoden zur Wissensentdeckung und Wissensrepräsentation (Approximation und Exploration von Wissen, Ordnungsstrukturen in Wissen, Ontologieentwicklung) in Daten als auch in der Analyse von (sozialen) Netzwerkdaten und damit verbundenen Wissensprozessen (Metriken in Netzwerken, Anomalieerkennung, Charakterisierung von sozialen Netzwerken). Dabei liegt ein Schwerpunkt auf der exakten algebraischen Modellierung der verwendeten Strukturen und auf der Evaluierung und Neuentwicklung von Netzwerkmaßen. Neben der Erforschung von Grundlagen in den Gebieten Ordnungs- und Verbandstheorie, Beschreibungslogiken, Graphentheorie und Ontologie werden auch Anwendungen – bspw. in sozialen Medien sowie in der Szientometrie – erforscht.

Das Fachgebiet Wissensverarbeitung ist Mitglied im Wissenschaftlichen Zentrum für Informationstechnik-Gestaltung (ITeG) der Universität Kassel, im Wissenschaftlichen Zentrum INCHER der Universität Kassel und im Forschungszentrum L3S.

Unsere neusten Publikationen

• 1.

  Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories. Scientometrics. (2021).

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
• 1.

  Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets. Accepted for publication at ICFCA 2021. (2021).

  Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic scaling.
• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Topic space trajectories. Scientometrics. (2021).

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
• 1.

  Stubbemann, L., Dürrschnabel, D., Refflinghaus, R.: Neural Networks for Semantic Gaze Analysis in XR Settings, http://arxiv.org/abs/2103.10451, (2021).

  Virtual-reality (VR) and augmented-reality (AR) technology is increasingly combined with eye-tracking. This combination broadens both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a novel approach which minimizes time and information necessary to annotate volumes of interest (VOIs) by using techniques from object recognition. To do so, we train convolutional neural networks (CNNs) on synthetic data sets derived from virtual models using image augmentation techniques. We evaluate our method in real and virtual environments, showing that the method can compete with state-of-the-art approaches, while not relying on additional markers or preexisting databases but instead offering cross-platform use.
• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Topic Space Trajectories: A case study on machine learning literature. Accepted for publication: Scientometrics. (2021).

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present emphtopic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
• 1.

  Dürrschnabel, D., Stumme, G.: Force-Directed Layout of Order Diagrams using Dimensional Reduction, http://arxiv.org/abs/2102.02684, (2021).

  Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
• 1.

  Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jaeschke, R., Stumme, G.: Proximity Dimensions and the Emergence of Collaboration: A HypTrails Study on German AI Research. Scientometrics. (2021).
• 1.

  Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. Scientometrics. (2021).

  Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
• 1.

  Stumme, G.: On Ordinal Data Science and its role in Socially Acceptable ICTDesign. In: Hentschel, A., Hornung, G., and Jandt, S. (eds.) Mensch – Technik – Umwelt: Verantwortung für eine sozialverträgliche Zukunft, Festschrift für Alexander Roßnagel zum 70. Geburtstag. pp. 181–198. Nomos, Baden-Baden (2020).

  Comparing and ordering things is a basal ability of mankind for organizing its physical and social environment. While many hierarchical relationships can be derived from numerical measures like length or voltage, many others cannot appropriately be captured this way. We argue that the newly emerging field of data science up to now lacks engagement in developing analysis methods for such ordinal data. By the example of an already existing approach in this domain, Formal Concept Analysis, we will discuss its capabilities as a knowledge representation and argue – based on its philosophical foundations – why it is an important building block for socially acceptable IT design.
• 1.

  Borchmann, D., Hanika, T., Obiedkov, S.: Probably approximately correct learning of Horn envelopes from queries. Discrete Applied Mathematics. 273, 30–42 (2020).

  We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
• 1.

  Hanika, T., Hirth, J.: Knowledge Cores in Large Formal Contexts, http://arxiv.org/abs/2002.11776, (2020).

  Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called \($k$\)-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study \($k$\)-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.
• 1.

  Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. In: Berthold, M.R., Feelders, A., and Krempl, G. (eds.) Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. pp. 496–508. Springer (2020).
• 1.

  Felde, M., Hanika, T., Stumme, G.: Null Models for Formal Contexts. Information. 11, 135 (2020).
• 1.

  Schäfermeier, B., Stumme, G., Hanika, T.: Topic Space Trajectories: A case study on machine learning literature, http://arxiv.org/abs/2010.12294, (2020).

  The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present emphtopic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic Dimension of Geometric Data Sets. Accepted for publication in: Tohoku Mathematical Journal. (2020).

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
• 1.

  Hanika, T., Hirth, J.: On the Lattice of Conceptual Measurements. (2020).

  We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
• 1.

  Kibanov, M., Heiberger, R.H., Rödder, S., Atzmueller, M., Stumme, G.: Social studies of scholarly life with sensor-based ethnographic observations. Scientometrics. (2019).

  Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.
• 1.

  Felde, M., Hanika, T.: Formal Context Generation Using Dirichlet Distributions. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 57–71. Springer (2019).
• 1.

  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata. In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) ICFCA. pp. 315–323. Springer (2019).
• 1.

  Hanika, T., Hirth, J.: Conexp-Clj - A Research Tool for FCA. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 70–75. CEUR-WS.org (2019).
• 1.

  Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis, http://arxiv.org/abs/1911.11496, (2019).

  Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic analysis recent approaches like word2vec or node2vec are well established tools in this realm. In the present paper we add to this line of research by introducing fca2vec, a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.
• 1.

  Hanika, T.: Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis., (2019).
• 1.

  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata, http://arxiv.org/abs/1902.00916, (2019).

  Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling in Wikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wikidata in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.
• 1.

  Hanika, T., Herde, M., Kuhn, J., Leimeister, J.M., Lukowicz, P., Oeste-Reiß, S., Schmidt, A., Sick, B., Stumme, G., Tomforde, S., Zweig, K.A.: Collaborative Interactive Learning - A clarification of terms and a differentiation from other research fields. CoRR. abs/1905.07264, (2019).
• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: DimDraw - A Novel Tool for Drawing Concept Lattices. In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 60–64. CEUR-WS.org (2019).
• 1.

  Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. In: Endres, D., Alam, M., and Sotropa, D. (eds.) ICCS. pp. 102–116. Springer (2019).
• 1.

  Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping. 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12--14, 2019, Houston, TX, USA (2019).

  For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.
• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension, http://arxiv.org/abs/1906.06208, (2019).

  Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding graph.
• 1.

  Dürrschnabel, D., Hanika, T., Stumme, G.: Drawing Order Diagrams Through Two-Dimension Extension. CoRR. abs/1906.06208, (2019).
• 1.

  Felde, M., Stumme, G.: Interactive Collaborative Exploration using Incomplete Contexts, https://arxiv.org/abs/1908.08740, (2019).

  A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.
• 1.

  Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. CoRR. abs/1907.09239, (2019).
• 1.

  Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis. CoRR (accepted as a chapter of the Springer volume titled "Complex Data Analytics with Formal Concept Analysis"). abs/1911.11496, (2019).
• 1.

  Schaefermeier, B., Hanika, T., Stumme, G.: Distances for wifi based topological indoor mapping. Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM (2019).
• 1.

  Hanika, T., Marx, M., Stumme, G.: Discovering Implicational Knowledge in Wikidata. In: Cristea, D., Ber, F.L., and Sertkaya, B. (eds.) Formal Concept Analysis - 15th International Conference, ICFCA 2019, Frankfurt, Germany, June 25-28, 2019, Proceedings. pp. 315–323. Springer (2019).
• 1.

  Hanika, T., Koyda, M., Stumme, G.: Relevant Attributes in Formal Contexts. CoRR. abs/1812.08868, (2018).
• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic Dimension of Geometric Data Sets, http://arxiv.org/abs/1801.07985, (2018).

  The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our mathematical model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
• 1.

  Navarro Bullock, B., Hotho, A., Stumme, G.: Accessing Information with Tags: Search and Ranking. In: Brusilovsky, P. and He, D. (eds.) Social Information Access: Systems and Technologies. pp. 310–343. Springer International Publishing, Cham (2018).

  With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
• 1.

  Doerfel, S., Hanika, T., Stumme, G.: Clones in Graphs. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., and Ras, Z.W. (eds.) ISMIS. pp. 56–66. Springer (2018).
• 1.

  Schäfermeier, B., Hanika, T., Stumme, G.: Distances for WiFi Based Topological Indoor Mapping, http://arxiv.org/abs/1809.07405, (2018).

  For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In particular, we show that among the considered distance measures the Earth Mover's Distance seems the most beneficial for the localization task. Combined with kernel density estimation we were able to retain the topological structure of rooms in a real-world office scenario.
• 1.

  Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension of concept lattices. CoRR. abs/1801.07985, (2018).

  Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm to a concrete data set is prone to the curse of dimensionality. To this end we extend V.~Pestov's axiomatic approach to the instrinsic dimension of data sets, based on the seminal work by M.~Gromov on concentration phenomena, and provide an adaptable and computationally feasible model for studying observable geometric invariants associated to features that are natural to both the data and the learning procedure. In detail, we investigate data represented by formal contexts and give first theoretical as well as experimental insights into the intrinsic dimension of a concept lattice. Because of the correspondence between formal concepts and maximal cliques in graphs, applications to social network analysis are at hand.
• 1.

  Kibanov, M., Becker, M., Müller, J., Atzmueller, M., Hotho, A., Stumme, G.: Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data. Proc. 33rd ACM Symposium On Applied Computing. ACM Press, New York, NY, USA (2018).
• 1.

  Axenovich, M., Duerrschnabel, D.: Subsets of vertices of the same size and the same maximum distance. 5, (2018).
• 1.

  Atzmueller, M., Thiele, L., Stumme, G., Kauffeld, S.: Analyzing Group Interaction on Networks of Face-to-Face Proximity using Wearable Sensors. Proc. IEEE International Conference on Future IoT Technologies. IEEE Press, Boston, MA, USA (2018).
• 1.

  Thiele, L., Atzmueller, M., Stumme, G., Kauffeld, S.: Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Developmental Peer Network Relationships. Psychology. (2018).
• 1.

  Doerfel, S., Hanika, T., Stumme, G.: Clones in Social Networks. CoRR. abs/1802.07849, (2018).

  It is well known that any bipartite (social) network can be regarded as a formal context \($(G,M,I)$\). Therefore, such networks give raise to formal concept lattices which can be investigated utilizing the toolset of Formal Concept Analysis (FCA). In particular, the notion of clones in closure systems on \($M$\), i.e., pairwise interchangeable attributes that leave the closure system unchanged, suggests itself naturally as a candidate to be analyzed in the realm of FCA based social network analysis. In this study, we investigate the notion of clones in social networks. After building up some theoretical background for the clone relation in formal contexts we try to find clones in real word data sets. To this end, we provide an experimental evaluation on nine mostly well known social networks and provide some first insights on the impact of clones. We conclude our work by nourishing the understanding of clones by generalizing those to permutations of higher order.
• 1.

  Schmidt, A., Stumme, G.: Prominence and Dominance in Networks. In: Faron Zucker, C., Ghidini, C., Napoli, A., and Yannick, T. (eds.) Proceedings of the 21th International Conference on Knowledge Engineering and Knowledge Management (EKAW). pp. 370–385. Springer (2018).
• 1.

  Demel, A., Dürrschnabel, D., Mchedlidze, T., Radermacher, M., Wulf, L.: A Greedy Heuristic for Crossing-Angle Maximization. In: Biedl, T.C. and Kerren, A. (eds.) Graph Drawing. pp. 286–299. Springer (2018).
• 1.

  Thiele, L., Atzmueller, M., Stumme, G., Kauffeld, S.: Frequent and/or Durable? The Predictive Impact of Initial Face-to-Face Contacts on the Formation and Evolution of Students’ Developmental Peer Network Relationships. Psychology. 09, 633–654 (2018).
• 1.

  Atzmueller, M., Thiele, L., Stumme, G., Kauffeld, S.: Contact Patterns, Group Interaction and Dynamics on Socio-Behavioral Multiplex Networks. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017). Eindhoven University of Technology, Eindhoven, The Netherlands (2017).
• 1.

  Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D.: HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses. New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016. Springer Verlag, Berlin/Heidelberg, Germany (2017).
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  Mueller, J., Stumme, G.: Predicting Rising Follower Counts on Twitter Using Profile Information. 9th International ACM Web Science Conference 2017 (WebSci 2017), Troy, NY, USA, June 26-28, 2017. Accepted for Publication. ACM, New York, NY, USA (2017).

  When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.
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  Sîrbu, A., Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F., Van den Bossche, J.: Participatory Patterns in an International Air Quality Monitoring Initiative. PLOS ONE. 10, 1–19 (2015).

  The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.
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  Scholz, C., Atzmueller, M., Kibanov, M., Stumme, G.: Predictability of Evolving Contacts and Triadic Closure in Human Face-to-Face Proximity Networks. Journal of Social Network Analysis and Mining. 4, (2014).
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  Scholz, C., Illig, J., Atzmueller, M., Stumme, G.: On the Predictability of Talk Attendance at Academic Conferences (Extended Abstract). Proc. LWA 2014 (KDML Special Track). RTWH Aachen University, Aachen, Germany (2014).
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  Macek, B.-E., Atzmueller, M., Stumme, G.: Predicting the Stability of User Interaction Ties in Twitter. Proc. I-KNOW 2014. ACM Press, New York, NY, USA (2014).
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  Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract). Proc. LWA 2013 (KDML Special Track). University of Bamberg, Bamberg, Germany (2013).
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  Scholz, C., Atzmueller, M., Kibanov, M., Stumme, G.: How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks. In: Rokne, J.G. and Faloutsos, C. (eds.) Proceedings of the 2013 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, Canada, August 25-28, 2013. pp. 356–363. ACM, New York, NY, USA (2013).

  Understanding the process of link creation is rather important for link prediction in social networks. Therefore, this paper analyzes contact structures in networks of face-to-face spatial proximity, and presents new insights on the dynamic and static contact behavior in such real world networks. We focus on face-to-face contact networks collected at different conferences using the social conference guidance system Conferator. Specifically, we investigate the strength of ties and its connection to triadic closures in face-to-face proximity networks. Furthermore, we analyze the predictability of all, new and recurring links at different points of time during the conference. In addition, we consider network dynamics for the prediction of new links.
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  Kibanov, M., Atzmueller, M., Scholz, C., Stumme, G.: On the Evolution of Contacts and Communities in Networks of Face-to-Face Proximity. In: Guerrero, J.E. (ed.) Proceedings of the 2013 IEEE Cyber, Physical and Social Computing, CPSCom 2013, Beijing, China, 20-23 August, 2013. pp. 993–1000. IEEE Computer Society, Los Alamitos, CA, USA (2013).

  Communities are a central aspect in the formation of social interaction networks. In this paper, we analyze the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific conferences. We compare the basic properties of the contact graphs to describe the properties of the contact networks and analyze the resulting community structure using state-of-the-art automic community detection algorithms. Specifically, we analyze the evolution of contacts and communities over time to consider the stability of the respective communities. In addition, we assess different factors which have an influence on the quality of community prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.
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  Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: On the Semantics of User Interaction in Social Media (Extended Abstract). Proc. LWA 2013 (KDML Special Track). University of Bamberg, Bamberg, Germany (2013).
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  Scholz, C., Atzmueller, M., Stumme, G.: Analyzing the Predictability of Human Contacts: On Influence Factors and Stronger Ties (Extended Abstract). Proc. LWA 2012 (KDML Special Track). University of Dortmund, Dortmund, Germany (2012).
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  Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: Observing Social and Physical Activities. Proceedings of the 2012 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012. pp. 317–324. IEEE Computer Society, Los Alamitos, CA, USA (2012).

  The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.
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  Atzmüller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing Social Interactions at Conferences. it - Information Technology. 53, 101–107 (2011).
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  Atzmüller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing Social Interactions at Conferences. it - Information Technology. 53, 101–107 (2011).
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  Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Towards Mining Semantic Maturity in Social Bookmarking Systems. In: Passant, A., Fernández, S., Breslin, J., and Bojārs, U. (eds.) Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011) (2011).
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  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Structure and Consistency: Assessment of Social Bookmarking Communities. Proceedings from Sunbelt XXXI. , Trade Winds Beach Resort (2011).
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  Hotho, A., Stumme, G.: From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research. In: Fensel, D. (ed.) Foundations for the Web of Information and Services. pp. 143–153. Springer (2011).
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  Atzmüller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.-E., Mitzlaff, F., Stumme, G.: Enhancing Social Interactions at Conferences. it - Information Technology, (53)3:101--107, Oldenbourg Wissenschaftsverlag GmbH, May 2011. (2011).
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  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment using Evidence Networks. Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010). , Barcelona, Spain (2010).

  Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.
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  Hotho, A., Benz, D., Eisterlehner, F., Jäschke, R., Krause, B., Schmitz, C., Stumme, G.: Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler. HMD -- Praxis der Wirtschaftsinformatik. Heft 271, 47–58 (2010).

  Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.
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  Hotho, A., Benz, D., Eisterlehner, F., Jäschke, R., Krause, B., Schmitz, C., Stumme, G.: Publikationsmanagement mit BibSonomy - ein Social-Bookmarking-System für Wissenschaftler. HMD - Praxis der Wirtschaftsinformatik. 271, 47–58 (2010).

  Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente wie Browsing und Suche am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.
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  Benz, D., Hotho, A., Stumme, G.: Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge. Proceedings of the 2nd Web Science Conference (WebSci10). , Raleigh, NC, USA (2010).
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  Benz, D., Hotho, A., Jäschke, R., Stumme, G., Halle, A., Lima-Gerlach, A.S., Steenweg, H., Stefani, S.: Academic Publication Management with PUMA - collect, organize and share publications. Proceedings of the 14. European Conference on Research and Advanced Technology for Digital Libraries. pp. 417–420 (2010).
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  Körner, C., Benz, D., Hotho, A., Strohmaier, M., Stumme, G.: Social Bookmarking Systems: Verbosity Improves Semantics. Proceedings of INSNA Sunbelt XXX. , Riva del Garda Fierecongressi, Trento, Italy (2010).
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  Benz, D., Hotho, A., Jäschke, R., Stumme, G., Halle, A., Lima-Gerlach, A.S., Steenweg, H., Stefani, S.: Academic Publication Management with PUMA - collect, organize and share publications. Proceedings of the 14. European Conference on Research and Advanced Technology for Digital Libraries. pp. 417–420 (2010).

  The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.
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  Hotho, A., Stumme, G.: Mining the World Wide Web. Künstliche Intelligenz. 5–8 (2007).
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  Catutto, C., Schmitz, C., Baldassarri, A., Servedio, V.D.P., Loreto, V., Hotho, and A., Grahl, M., Stumme, G.: Network Properties of Folksonomies. AI Communications Journal, Special Issue on "Network Analysis in Natural Sciences and Engineering". (2007).
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  Ganter, B., Stumme, G., Wille, R. eds.: Formal Concept Analysis: Foundations and Applications. Springer (2005).
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  Stumme, G., Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Sure, Y., Volz, R., Zacharias, V.: The Karlsruhe view on ontologies. University of Karlsruhe, Institute AIFB (2003).
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  Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Stumme, G., Sure, Y., Tane, J., Volz, R., Zacharias, V.: KAON - Towards a large scale Semantic Web. In: Bauknecht, K., Tjoa, A.M., and Quirchmayr, G. (eds.) Proc. E-Commerce and Web Technologies, Third International Conference, EC-Web 2002. Springer, Aix-en-Provence %, France (2002).